Graph databases typically handle queries involving traversing relationships using specialized graph query languages like Cypher. Cypher is designed to express patterns and relationships in the graph, making it more intuitive and efficient for querying graph databases compared to traditional SQL queries.

Composite indexing is an approach where the index is built on multiple columns. This technique is beneficial for optimizing query performance, especially when queries involve those specific columns.

Type 3 SCD is advantageous as it captures both current and historical data. It maintains a separate column for historical changes, providing a snapshot of changes over time without excessive storage requirements. This is particularly useful for scenarios where a limited history is needed.

To optimize performance in a Key-Value Store, all of the above techniques may be employed. Caching helps reduce the need to fetch data from the underlying storage repeatedly, indexing improves lookup speed, and compression reduces the amount of data transferred, collectively enhancing overall system performance.

The correct term is Functionally Dependent. An attribute is functionally dependent on the primary key if its value is uniquely determined by the entire primary key, not just a proper subset. Understanding this concept is crucial for database design and normalization.

Clustering in the context of databases refers to a technique for organizing data into groups (clusters) based on similarity. This helps in organizing data efficiently and can improve query performance by reducing the amount of data that needs to be processed.

Database design tools, in addition to ERD design, often include features for report generation. These tools enable users to create detailed reports based on the data model, providing valuable insights into the structure and relationships within the database. This aids in better understanding and communication of the database design.

Sharding is a technique used to improve query performance by limiting the partitions that need to be scanned. It involves dividing a large database into smaller, more manageable parts called shards, which can be processed independently.

For a recommendation system based on interconnected data, a Graph Database would be most suitable. Graph databases excel at representing and querying relationships between entities, making them ideal for scenarios where the connections between data points are crucial, such as social networks.

Each row in a column-family store is identified by a unique key. This key is used to retrieve and distinguish the row from others in the same family. The key is crucial for efficient data retrieval in column-family stores.

A Primary Key uniquely identifies a record within a table. It ensures that each row in the table is unique, and it serves as the basis for establishing relationships with other tables.

Query caching plays a crucial role in database performance tuning by storing the results of frequently executed queries in memory. This reduces the need to re-execute identical queries, thereby improving response time and overall system performance. However, it is essential to manage query caching effectively to ensure that cached data remains consistent and up-to-date.